Abstract: A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Abstract: A non-transitory computer-readable storage medium storing a set of instructions executable by a processor. The set of instructions is operable to receive a current patient set of data relating to a current patient; compare the current patient set of data to a plurality of previous patient sets of data, each of the previous patient sets of data corresponding to a previous patient; select one of the previous patient sets of data based on a level of similarity between the selected previous patient set of data and the current patient set of data; and provide the selected previous patient set of data to a user.

Abstract: The invention relates to search for cases in a database. According to the proposed method and apparatus, similarity matching is performed between an input case and a set of cases in an initial search to receive similar cases by using a given matching criterion. Then statistics on image and/or non-image-based features associated with the similar cases are calculated and presented to the user with the similar cases. In a search refinement the similar cases are refined by additional features that are determined by the user based on the statistics. The search refinement can be iterative depending on the user's need.

Abstract: A system and method of providing computer-aided analysis of medical images uses an image processor (910) to process medical image data. A decision engine (920) generates a diagnosis based on the image data (940). The decision engine estimates the probability of an illness based on the image data and assesses the relevance of any unavailable data. The result is used to request this unavailable data from the user for computing a more complete diagnosis or otherwise displaying the results in incomplete form due to either the lack of additional data or the confidence in the incomplete diagnostic results. The diagnostic results may be displayed on an output terminal (970) and may be stored in the database (930).

Abstract: A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Abstract: A method, system, and computer program product for disease simulation and identification in medical images. The method generates a set of synthetic projection images from first medical imaging data of a first imaging type. Second medical imaging data of a second imaging type is projected onto the set of synthetic projection images. One or more synthetic images of the set of synthetic projection images are modified to generate a set of modified projection images based on the second medical imaging data. The method generates an imaging model based on the set of modified projection images. The method obtains a patient medical image of the first imaging type and identifies an attribute of interest on the patient medical image based on the imaging model.

Abstract: Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for identifying symptoms based on digital media. The method includes one or more processors receiving digital media and information associated with a first animal from a user. The method further includes one or more processors identifying data records, stored in a knowledge database, that are respectively associated with an animal that is similar to the first animal. The method further includes one or more processors determining symptom information corresponding to the first animal based on a comparison of the received digital media and information associated with the first animal and the identified data records. The method further includes presenting the determined symptom information to a user.

Abstract: A method generates images based on context-aware imaging. The method includes acquiring a first image of a patient using first image acquisition parameters. The method includes determining, within the first image, an area as a function of first data associated with the patient. The method includes determining modification data as a function of at least one of (a) second data corresponding to the determined area and (b) the first data. The method includes determining second image acquisition parameters as a function of the modification data and the first image acquisition parameters. The method includes acquiring a second image of the patient using the second image acquisition parameters.

Abstract: A method includes obtaining, in electronic format, an image (102) including a medical image display region (104) and an information display region (106). The at least one of the medical image display region or the information display region includes graphical indicia representing at least one of an annotation (110, 112, 114, 116) or alphanumeric information (118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140). The method further includes evaluating pixels of the image to identify pixels including the graphical indicia representing an annotation or alphanumeric information of interest in the image. The method further includes extracting the annotation or alphanumeric information of interest from the identified graphical indicia from the image. The method further includes inserting the extracted annotation or alphanumeric information of interest in an electronically formatted clinical report for the image.

Abstract: When evaluating patient cases to determine complexity thereof, a computer-aided stratification technique is applied to analyze historical patient case diagnoses and correctness thereof in order to calculate a stratification score (20) for each of a plurality of abnormality types and/or anatomical locations. When a new patient case is received, the computer-aided stratification technique is applied to evaluate the patient case in view of historical data and assign a stratification score thereto. A ranked list (21) of current patient cases can be generated according to stratification scores, and physician workload can be adjusted as a function thereof so that workload is balanced across physicians and/or according to physician experience level.

Abstract: Mechanisms are provided to implement a cognitive artificial intelligence training mechanism for simulating patients for developing artificial intelligence based medical solutions. The cognitive artificial intelligence training mechanism perturbs non-image based information of a real patient from a real patient data set forming perturbed non-image based information. The cognitive artificial intelligence training mechanism generates an artificial patient data in an artificial patient data set using the perturbed non-image based information and a non-perturbed medical image of the real patient. The cognitive artificial intelligence training mechanism then trains an operation of a learning algorithm utilized by the cognitive data processing system using real patient data in the real patient data set and the artificial patient data in the artificial patient data set.

Abstract: Mechanisms are provided to implement a cognitive artificial intelligence training mechanism for simulating patients for developing artificial intelligence based medical solutions. The cognitive artificial intelligence training mechanism perturbs non-image based information of a real patient from a real patient data set forming perturbed non-image based information, The cognitive artificial intelligence training mechanism generates an artificial patient data in an artificial patient data set using the perturbed non-image based information and a non-perturbed medical image of the real patient. The cognitive artificial intelligence training mechanism then trains an operation of a learning algorithm utilized by the cognitive data processing system using real patient data in the real patient data set and the artificial patient data in the artificial patient data set.

Abstract: A clinical decision support (CDS) system comprises: a case grouping sub-system (10) including a graphical user interface (30) that is operative to simultaneously display data representing a plurality of patient cases and further configured to enable a user to group selected patient cases represented by the simultaneously displayed data into clinically related groups (32) as selected by the user; a probative features determination sub-system (12) that is operative to determine probative features (44) that correlate with the clinically related groups; and a CDS user interface (16) that is operative to receive current patient data relating to a current patient case and to output clinical decision support information based on values of the probative features determined from the received current patient data.

Abstract: A system and method for cross-modality case-based computer-aided diagnosis comprises storing a plurality of cases, each case including at least one image of one of a plurality of modalities and non-image information, mapping a feature relationship between a feature from images of a first modality to a feature from images of a second modality, and storing the relationship.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for identifying symptoms based on digital media. The method includes one or more processors receiving digital media and information associated with a first animal from a user. The method further includes one or more processors identifying data records, stored in a knowledge database, that are respectively associated with an animal that is similar to the first animal. The method further includes one or more processors determining symptom information corresponding to the first animal based on a comparison of the received digital media and information associated with the first animal and the identified data records. The method further includes presenting the determined symptom information to a user.

Abstract: An oncology monitoring system comprises: an image analysis module (42, 44) configured to perform an oncological monitoring operation based on images of a subject, for example acquired by positron emission tomography (PET) and computed tomography (CT); and a clinical guideline support module (10). The clinical guideline support module is configured to: display a graphical flow diagram (GFD) of a clinical therapy protocol for treating the subject comprising graphical blocks (B0, B1, B2, B3, B4, B5, B21, B211, B22, B221, B222, B223, B23, B231, B232) representing therapeutic or monitoring operations of the clinical therapy protocol including at least one monitoring operation performed by the image analysis module; annotate a graphical block of the graphical flow diagram with subject-specific information pertaining to a therapeutic or monitoring operation represented by the graphical block; and display an annotation (POP) of a graphical block (B211) responsive to selection of the graphical block by a user.

Abstract: A method includes determining, with a computer implemented classifier, a health state of tissue interest of a subject in at least one pair of images of the subject based on a predetermined set of longitudinal features of the tissue of interest of the subject in the at least one pair of images of the subject. The at least one pair of images of the subject includes a first image of the tissue of interest acquired at a first moment in time and a second image of the tissue of interest acquired at a second moment in time. The first and second moments in time are different moments in time. The method further includes visually displaying indicia indicative of the determined health state.

Abstract: Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Abstract: A method includes determining, with a computer implemented classifier, a health state of tissue interest of a subject in at least one pair of images of the subject based on a predetermined set of longitudinal features of the tissue of interest of the subject in the at least one pair of images of the subject. The at least one pair of images of the subject includes a first image of the tissue of interest acquired at a first moment in time and a second image of the tissue of interest acquired at a second moment in time. The first and second moments in time are different moments in time. The method further includes visually displaying indicia indicative of the determined health state.